Last updated: 2022-04-28

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Knit directory: CarolineNCC1/

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File Version Author Date Message
Rmd 32e6500 LucianoRogerio 2022-04-28 Removed Traits with higher % of missing data
Rmd 05dacab LucianoRogerio 2021-12-19 Update diversity analysis
html 05dacab LucianoRogerio 2021-12-19 Update diversity analysis
Rmd 9f19f12 LucianoRogerio 2021-11-22 update website
html 9f19f12 LucianoRogerio 2021-11-22 update website
Rmd 3fe13ea LucianoRogerio 2021-11-22 Improvement for the layout of the website
html 3fe13ea LucianoRogerio 2021-11-22 Improvement for the layout of the website
Rmd 3ecd450 LucianoRogerio 2021-11-08 Final graphs for Descritive analysis
html 3ecd450 LucianoRogerio 2021-11-08 Final graphs for Descritive analysis
Rmd a5ffd57 LucianoRogerio 2021-11-08 Final graphs for Descritive analysis
html a5ffd57 LucianoRogerio 2021-11-08 Final graphs for Descritive analysis
Rmd 15bf4bb LucianoRogerio 2021-10-25 Fix and add link for the html pages
html 15bf4bb LucianoRogerio 2021-10-25 Fix and add link for the html pages
html 1fc7d3a LucianoRogerio 2021-10-25 Build site.
Rmd e880482 LucianoRogerio 2021-10-25 First Boxplot Graphs and Html pages

Boxplot de coleções nucleares - Características Quantitativas

Obter Dados Fenotípicos

Coleções nucleares - Dados Fenotipicos

suppressMessages(library(tidyverse)); suppressMessages(library(here)); suppressMessages(library(reshape2))
suppressMessages(library(reactable))

Method1 <- read.table(here::here("output", "CCPhenData", "DadosCCPheno1.CSV"), header = T,
                      sep = ";", dec = ".",
                      colClasses = c(rep("character", times = 31),
                                    rep("numeric", times = 3),
                                    "character",
                                    rep("numeric", times = 16)))


Method2 <- read.table(here::here("output", "CCPhenData", "DadosCCPheno2.CSV"), header = T,
                      sep = ";", dec = ".",
                      colClasses = c(rep("character", times = 31),
                                    rep("numeric", times = 3),
                                    "character",
                                    rep("numeric", times = 16)))


Method3 <- read.table(here::here("output", "CCPhenData", "DadosCCPheno3.CSV"), header = T,
                      sep = ";", dec = ".",
                      colClasses = c(rep("character", times = 31),
                                    rep("numeric", times = 3),
                                    "character",
                                    rep("numeric", times = 16)))

Coleções Nucleares - dados Moleculares

Method4 <- read.table(here::here("output", "CCGenData", "DadosCCGeno1.CSV"), header = T, 
                      sep = ";", dec = ".",
                      colClasses = c(rep("character", times = 31),
                                    rep("numeric", times = 3),
                                    "character",
                                    rep("numeric", times = 16)))


Method5 <- read.table(here::here("output", "CCGenData", "DadosCCGeno2.CSV"), header = T, 
                      sep = ";", dec = ".",
                      colClasses = c(rep("character", times = 31),
                                    rep("numeric", times = 3),
                                    "character",
                                    rep("numeric", times = 16)))


Method6 <- read.table(here::here("output", "CCGenData", "DadosCCGeno3.CSV"), header = T, 
                      sep = ";", dec = ".",
                      colClasses = c(rep("character", times = 31),
                                    rep("numeric", times = 3),
                                    "character",
                                    rep("numeric", times = 16)))

Method7 <- read.table(here::here("output", "CCGenData", "DadosCCGeno4.CSV"), header = T,
                      sep = ";", dec = ".",
                      colClasses = c(rep("character", times = 31),
                                    rep("numeric", times = 3),
                                    "character",
                                    rep("numeric", times = 16)))


Method8 <- read.table(here::here("output", "CCGenData", "DadosCCGeno5.CSV"), header = T,
                      sep = ";", dec = ".",
                      colClasses = c(rep("character", times = 31),
                                    rep("numeric", times = 3),
                                    "character",
                                    rep("numeric", times = 16)))


Method9 <- read.table(here::here("output", "CCGenData", "DadosCCGeno6.CSV"), header = T,
                      sep = ";", dec = ".",
                      colClasses = c(rep("character", times = 31),
                                    rep("numeric", times = 3),
                                    "character",
                                    rep("numeric", times = 16)))

Method10 <- read.table(here::here("output", "CCGenData", "DadosCCGeno7.CSV"), header = T,
                       sep = ";", dec = ".",
                       colClasses = c(rep("character", times = 31),
                                    rep("numeric", times = 3),
                                    "character",
                                    rep("numeric", times = 16)))


Method11 <- read.table(here::here("output", "CCGenData", "DadosCCGeno8.CSV"), header = T,
                       sep = ";", dec = ".",
                      colClasses = c(rep("character", times = 31),
                                    rep("numeric", times = 3),
                                    "character",
                                    rep("numeric", times = 16)))

Method12 <- read.table(here::here("output", "CCGenData", "DadosCCGeno9.CSV"), header = T,
                       sep = ";", dec = ".",
                       colClasses = c(rep("character", times = 31),
                                    rep("numeric", times = 3),
                                    "character",
                                    rep("numeric", times = 16)))

Banco de Germoplasma de Mandioca da EMBRAPA Mandioca

AllBAG <- readRDS(file = here::here("output", "DataSelPreparedCar.rds"))

Adicionar informações do Método e tipo de dados utilizados na seleção da Coleção Nuclear

AllBAG$Data <- rep("BAG", times = nrow(AllBAG))
AllBAG$Method <- rep("BAG", times = nrow(AllBAG))

Method1$Data <- rep("Pheno", times = nrow(Method1))
Method1$Method <- rep("GW/CH", times = nrow(Method1))

Method2$Data <- rep("Pheno", times = nrow(Method2))
Method2$Method <- rep("CH", times = nrow(Method2))

Method3$Data <- rep("Pheno", times = nrow(Method3))
Method3$Method <- rep("GW/MLST", times = nrow(Method3))

Method4$Data <- rep("Geno", times = nrow(Method4))
Method4$Method <- rep("MR/SH", times = nrow(Method4))

Method5$Data <- rep("Geno", times = nrow(Method5))
Method5$Method <- rep("CE/SH", times = nrow(Method5))

Method6$Data <- rep("Geno", times = nrow(Method6))
Method6$Method <- rep("MR/EH", times = nrow(Method6))

Method7$Data <- rep("Geno", times = nrow(Method7))
Method7$Method <- rep("CE/EH", times = nrow(Method7))

Method8$Data <- rep("Geno", times = nrow(Method8))
Method8$Method <- rep("MR/AC", times = nrow(Method8))

Method9$Data <- rep("Geno", times = nrow(Method9))
Method9$Method <- rep("CE/AC", times = nrow(Method9))

Method10$Data <- rep("Geno", times = nrow(Method10))
Method10$Method <- rep("AM/MLST", times = nrow(Method10))

Method11$Data <- rep("Geno", times = nrow(Method11))
Method11$Method <- rep("MR/MLST", times = nrow(Method11))

Method12$Data <- rep("Geno", times = nrow(Method12))
Method12$Method <- rep("CE/MLST", times = nrow(Method12))

Juntar todos os Dados das coleções nucleares e do BAG

Alldataset <- rbind(AllBAG, Method1, Method2, Method3,
                    Method4, Method5, Method6,
                    Method7, Method8, Method9,
                    Method10, Method11, Method12)

saveRDS(Alldataset, here::here("output", "AllDataCCCaroline.RDS"))

Preparar o data frame para fazer os boxplots

QualityTrait <- colnames(Alldataset)[sapply(X = Alldataset, class) == "character"] %>% 
  setdiff(., c("Acessos", "Data", "Method"))

AlldataSetQuant <- Alldataset %>% select(-all_of(QualityTrait))
AlldataSetQuali <- Alldataset %>% select(Acessos, all_of(QualityTrait), Data, Method)

AlldataSetQuant2 <- melt(AlldataSetQuant, id.vars = c("Acessos", "Data", "Method"),
       variable.name = "Trait", value.name = "Value")

Table 1. Formato da entrado do objeto para realizar o boxplot

AlldataSetQuant2$Data <- factor(AlldataSetQuant2$Data,
                                levels = c("BAG", "Pheno", "Geno"), ordered = T)
AlldataSetQuant2$Method <- factor(AlldataSetQuant2$Method,
                                levels = c("BAG", "GW/CH", "GW/MLST", "CH",
                                           "MR/SH", "MR/EH", "MR/AC", "MR/MLST",
                                           "CE/SH", "CE/EH", "CE/AC", "CE/MLST",
                                           "AM/MLST"),
                                ordered = T)
Remover os outliers
filter_lims <- function(x){
  l <- boxplot.stats(x)$stats[1]
  u <- boxplot.stats(x)$stats[5]

  for (i in 1:length(x)){
    x[i] <- ifelse(x[i]>l & x[i]<u, x[i], NA)
  }
  return(x)
}


AlldataSetQuant3 <- AlldataSetQuant2 %>% group_by(Trait, Method) %>%
  mutate(Value2 = filter_lims(Value))

Plotar o boxplot por conjunto de caracteres

Foliar1 <- c("ComprLobulo", "LargLobulo",
             "RelComprLar", "ComprPeciolo")
Foliar2 <- c("AP", "PPA", "IC", "PA")

Root <- c("EspEntreCasca", "ComprMedRzs", "DiamMedRzs", "HCNPic",
          "NR", "DRY", "DMCsg", "PTR")

Fig 1. Boxplot das características morfológicas de folha 1

Fig 2. Boxplot das características morfológicas de folha 2

Fig 3. Boxplot das características morfológicas de raiz

Barplot de coleções nucleares - Características Qualitativas

Preparar os dados qualitativos para plotar barplot
AlldataSetQuali2 <- melt(AlldataSetQuali, id.vars = c("Acessos", "Data", "Method"),
       variable.name = "Trait", value.name = "Value")


AlldataSetQuali3 <- AlldataSetQuali2 %>% filter (!is.na(Value)) %>%
  group_by(Trait, Data, Method) %>% summarise(N = table(Value, useNA = "no"),
                                              Score = names(N)) %>%
  mutate(N = as.numeric(N))
`summarise()` has grouped output by 'Trait', 'Data', 'Method'. You can override
using the `.groups` argument.
AlldataSetQuali3$Data <- factor(AlldataSetQuali3$Data,
                                levels = c("BAG", "Pheno", "Geno"), ordered = T)
AlldataSetQuali3$Method <- factor(AlldataSetQuali3$Method,
                                levels = c("BAG", "GW/CH", "GW/MLST", "CH",
                                           "MR/SH", "MR/EH", "MR/AC", "MR/MLST",
                                           "CE/SH", "CE/EH", "CE/AC", "CE/MLST",
                                           "AM/MLST"),
                                ordered = T)
AlldataSetQuali3$Score <- factor(AlldataSetQuali3$Score,
                                 levels = c(0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
                                            30, 40, 45, 50, 60, 70, 80, 85, 90,
                                            95, 100, 105, 110, 115, 120, 130,
                                            140, 150, 160))

Table 2. Formato da entrado do objeto para realizar o barplot

Separar as características em grupos para plotar
traitsFolha <- levels(AlldataSetQuali3$Trait)[c(1:4, 12:17, 20, 23)]
traitsCaule <- levels(AlldataSetQuali3$Trait)[c(5:8, 18:19, 21:22, 28, 32)]
traitsRaiz <- levels(AlldataSetQuali3$Trait)[c(9:11, 24:27, 29:31)]

traitsFolhaLimbo <- traitsFolha[c(1, 3:7)]
traitsFolhaPecio <- traitsFolha %>% setdiff(traitsFolhaLimbo)

traitsCauleCor <- traitsCaule[c(1:4)]
traitsCaule2 <- traitsCaule %>% setdiff(c(traitsCauleCor, "AnguloRamif"))

traitsRaizCor <- traitsRaiz[c(1:3, 8:9)]
traitsRaizFor <- traitsRaiz %>% setdiff(traitsRaizCor)

Fig 4. Barplot para cores do caule

Fig 5. Barplot para o segundo grupo de características morfológicas do caule

Fig 6. Barplot para Angulo de Ramificação das hastes do caule

Fig 7. Barplot de características morfológicas do Limbo foliar

Fig 8. Barplot de características morfológicas do pecíolo da folha

Fig 9. Barplot de cor e facilidade de destaque de epiderme da raiz

Fig 10. Barplot de características morfológicas de raiz

Back - Coleções Nucleares Genotípicas

Next - Estimativas de Diversidade genética

Home


sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Big Sur 11.6.5

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.1-arm64/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] reactable_0.2.3 reshape2_1.4.4  here_1.0.1      forcats_0.5.1  
 [5] stringr_1.4.0   dplyr_1.0.8     purrr_0.3.4     readr_2.1.2    
 [9] tidyr_1.2.0     tibble_3.1.6    ggplot2_3.3.5   tidyverse_1.3.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8.3      lubridate_1.8.0   assertthat_0.2.1  rprojroot_2.0.3  
 [5] digest_0.6.29     utf8_1.2.2        reactR_0.4.4      plyr_1.8.7       
 [9] R6_2.5.1          cellranger_1.1.0  backports_1.4.1   reprex_2.0.1     
[13] evaluate_0.15     highr_0.9         httr_1.4.2        pillar_1.7.0     
[17] rlang_1.0.2       readxl_1.4.0      rstudioapi_0.13   whisker_0.4      
[21] jquerylib_0.1.4   rmarkdown_2.14    labeling_0.4.2    htmlwidgets_1.5.4
[25] munsell_0.5.0     broom_0.8.0       compiler_4.1.2    httpuv_1.6.5     
[29] modelr_0.1.8      xfun_0.30         pkgconfig_2.0.3   htmltools_0.5.2  
[33] tidyselect_1.1.2  workflowr_1.7.0   viridisLite_0.4.0 fansi_1.0.3      
[37] crayon_1.5.1      tzdb_0.3.0        dbplyr_2.1.1      withr_2.5.0      
[41] later_1.3.0       grid_4.1.2        jsonlite_1.8.0    gtable_0.3.0     
[45] lifecycle_1.0.1   DBI_1.1.2         git2r_0.30.1      magrittr_2.0.3   
[49] scales_1.2.0      cli_3.3.0         stringi_1.7.6     farver_2.1.0     
[53] fs_1.5.2          promises_1.2.0.1  xml2_1.3.3        bslib_0.3.1      
[57] ellipsis_0.3.2    generics_0.1.2    vctrs_0.4.1       tools_4.1.2      
[61] glue_1.6.2        crosstalk_1.2.0   hms_1.1.1         fastmap_1.1.0    
[65] yaml_2.3.5        colorspace_2.0-3  rvest_1.0.2       knitr_1.38       
[69] haven_2.5.0       sass_0.4.1